The answer is basically to do with income and expenditure, of energy. Even at rest, we are remorselessly expending energy: if we don’t replace this energy, we die. If like corals or sea-anemones we were lucky enough to live in an environment where we were bombarded by food, we could just glue ourselves to rock and keep our mouths open. But for the big spenders, warm-blooded animals like us, the only way of keeping in surplus is to gamble. We expend a lot of energy as a stake, in order to perform actions from which we hope to get more in return, rather like a business investing some of its profit in the hope of even huger profits in the future. In a sense this decisionmaking – to do or not to do – is the most difficult task an organism has to undertake. As we shall see, the whole of the brain can usefully be thought of as a mechanism for reducing the risk, by making more and more accurate predictions about the likely result of any particular course of action, on the basis of past experience, stored not just in our brains, but in our books.
Motivational maps
But the decision process need not be as complex as this. In a simple creature – an amoeba is an extreme case – the nature of the fundamental mechanism is particularly obvious: its motivation is entirely a function of its immediate environment, sensed chemically: Consequently we see
tropisms in response to gradients of things like food (positive) or poisons (negative). On the one hand, attractive stimuli set up a positive gradient down which the animal moves; on the other hand, threatening conditions create a negative gradient, and it moves away. So the amoeba’s environment is a sort of motivational potential field or contour map, and the amoeba is like a little charged particle that moves around in response to
local gradients, the path it traces out being a direct function of its environment. Very simple tropistic mechanisms like these give rise to surprisingly life-like behaviour, as in Dr Grey Walter’s pioneering electromechanical tortoise Elsie which ran amiably around the floor looking for light, or the interactive version in NeuroLab.

Though higher animals produce more complex behaviour, the mechanism is essentially the same. The added complexity comes about for two reasons.
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First, because there are many more types of desirable and undesirable stimuli to which they may react, and many of them – perhaps most – are learnt: these are the secondary
motivators (like money) that through experience become associated with other more self-evidently desirable goals (
Chapter 8, p. 177). Consequently each individual has its own classification of stimuli into desirable and undesirable categories, unique because it is the result of that individual’s own personal experience.
Second, because whether a particular stimulus like food is a motivator or not depends also on ones own need – in this case, whether or not one is hungry. Motivation, in other words, is something like the product of gradient and need, so changing patterns of need give rise to changing patterns of activity even though the environment itself is the same, and to an outsider the resulting behaviour may appear to be complex or even unpredictable. Thus Cambridge for me consists of a large number of separate gradient or contour maps, each corresponding to a different need: one for food, with high points at all the food shops and restaurants, one for money, centred on my bank and supplemented by cash machines, one for newspapers, one for avoiding rain, and so on. Which one is operative at any particular moment depends on my need at that moment, rather like those electrical maps sometimes seen at the more down-market tourist resorts, with bulbs that light up when you press one of a set of buttons marked ‘parking’, ‘pubs’, ‘post offices’ and so on.
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So the fundamental limbic motivational computer has to be a sort of ‘yellow pages’ connecting particular needs with a kind of library of motivational maps of the outside world: like the tourist map, it translates information about need into the kind of tropistic data that can in turn be changed by the higher levels of the motor system into actual patterns of activity. Here A, B, C, etc. are separate needs such as hunger, thirst, etc., and each has its own stored motivational map that is activated in appropriate physiological circumstances. Some evidence suggests that these ‘yellow pages’ or motivational maps are embodied in the
hippocampus. Hippocampal neurons have been found in the rat that respond specifically when the animal is at a particular point in its environment, for example within a maze that the rat has learnt; its involvement in certain kinds of learning was discussed in
Chapter 13. In the example above, the shading represents average firing frequency of a unit in rat hippocampus at different points within an enclosure, showing that it seems to be
associated with a particular location. Similarly, brain scans of London-taxi drivers, who have to undergo a period of rigorous spatial learning, demonstrate an enlarged posterior hippocampus relative to matched controls.

Equally, it is the
hypothalamus that provides information about need, about the state of the body, and projects to the hippocampus via the septal nuclei. It is the centre to which autonomic afferents project, and its neurons monitor such physiological states of the blood as glucose concentration, temperature and osmolarity, as well as levels of circulating hormones, that decides one’s state of need. It is also in the hypothalamus that primary consummatory responses such as eating and drinking may be triggered off by electrical stimulation. The hypothalamus is thus utterly at the heart of the neural mechanisms that generate motivation: it is discussed in detail later in this chapter (
p. 278).
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It is natural to feel a certain resistance to the notion that our own richly complex lives, the apparent wealth of choices open to us, and our sense of liberty to choose among them, could possibly be determined by so simple a mechanism. But as Herbert Simon has said, human behaviour is really rather simple, but because most people live in very complex physical, man-made and social environments, their actual behaviour
appears extremely complicated; thus the path traced out by an ant moving over rough ground may be very complex in appearance, even though its behaviour is simply directed at getting back to its nest.

To some extent it is in fact possible to plot motivational maps in Man: by averaging over large numbers of individuals, it is not difficult to measure quite directly the same kinds of tropistic gradients for us humans, that work so well in describing what an amoeba does. If you take a group of people and ask them the very simple question ‘
Where in Britain would you like to be?’, it is possible to obtain contour maps of average preferences, in this case of the relative desirability amongst a group of school-leavers of different parts of the country. These are certainly motivational maps, in the sense that if the individuals had the means to do it, they would be translated into actual migratory behaviour not very different in essence from our amoeba moving blindly down its tropistic gradient.
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